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Analyzed about 7 hours ago. based on code collected 1 day ago.
Nov 05, 2025 — Nov 05, 2026
Commit Message Contributor Files Modified Lines Added Lines Removed Code Location Date
deleted asferclassifiers.h~
Srinivasan Kannan
as K.Srinivasan
More... almost 9 years ago
------------------------------------------------------------------------------------------------------------------------------------------- 503. (FEATURE-DONE) Scheduler Analytics - Process kNN classification and K-Means clustering, Process Dict Hashing - 25 January 2018 (Only in NeuronRain Enterprise - GitHub) ------------------------------------------------------------------------------------------------------------------------------------------- 1. python-src/software_analytics/DeepLearning_SchedulerAnalytics.py has been updated to define a getHash() function similar to Streaming_<algorithm>.py implementations, which creates an MD5 hash of process psutils dictionary string. 2. This hashing of dictionary is because of huge size of process dictionary storing of which requires few KBs for each process. Computing distance (Levenshtein) of these strings is again memory intensive. 3. DictDiffer distance implemented in python-src/software_analytics/DeepLearning_SchedulerAnalytics.py is not directly invocable in C++ and diff between process dictionary strings is also huge in KBs. 4. Succinct representation of process dictionary string - Fingerprinting - is therefore a necessity to optimize space. MD5 binary hash digest of the process dictionary string is written to python-src/software_analytics/asfer.enterprise.encstr.scheduleranalytics prefixed by process name and process id , instead of complete chunk. 5. cpp-src/asferkNNclustering.cpp and cpp-src/asferkmeansclustering.cpp are updated for EOF check and number of clusters has been increased to 10 in kNN classifier. 6. asfer binary has been rebuilt and asfer.conf has been updated to do clustering and classification. 7. process statistics dataset - cpp-src/asfer.enterprise.encstr, cpp-src/asfer.enterprise.encstr.clustered and cpp-src/asfer.enterprise.encstr.kNN have been updated. 8. asfer kNN classification and KMeans clustering has been executed on this encoded process statistics dataset 9. logs have been committed to cpp-src/testlogs/asfer.SchedulerAnalytics.KMeansAndkNNClustering.log.25January2018 (Scheduler Analytics) and cpp-src/testlogs/asfer.SchedulerAnalytics.KMeansAndkNNClustering.log.25January2018 (kNN classification and KMeans clustering) - for 194 processes 10. logs for clustering and classification imply similar processes flock together -
Srinivasan Kannan
More... almost 9 years ago
Updated AsFer Design Document - SIRI reference
Srinivasan Kannan
More... almost 9 years ago
Updated AsFer Design Document - Typo
Srinivasan Kannan
More... almost 9 years ago
Updated AsFer Design Document - Factorization
Srinivasan Kannan
More... almost 9 years ago
Updated Requirements.txt
Srinivasan Kannan
More... almost 9 years ago
Updated AsFer Design Document - Scheduler Analytics
Srinivasan Kannan
More... almost 9 years ago
------------------------------------------------------------------------------------------------------------------------------------------ Scheduler Analytics - Interprocess Distance Computation by DictDiffer ------------------------------------------------------------------------------------------------------------------------------------------ 1. Representing OS Process Information as Feature Vectors has been mentioned as Software Scheduler Analytics usecase in http://neuronrain-documentation.readthedocs.io/en/latest/ 2. This commit implements a new python function which reads process info as a dictionary from psutils. Psutils has an option to read individual process metrics or to read the dictionary in its entirety. 3. Psutils per-process dictionary has all the basic details pertaining to instantaneous resource consumptions of a process 4. These per-process features are collated in an array and are json.dump()-ed as strings in asfer.enterprise.encstr.scheduleranalytics file which can be used as input by clustering/classification NeuronRain-AsFer C++ implementations. 5. logs for this have been committed to software_analytics/testlogs/DeepLearning_SchedulerAnalytics.log.24January2018 6. Distance between two process feature dictionaries/vectors are printed by DictDiffer which has been imported and length of the diff is the distance function between any two processes.
Srinivasan Kannan
More... almost 9 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... almost 9 years ago
Updated AsFer Design Document - Answer-Questioning
Srinivasan Kannan
More... almost 9 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... almost 9 years ago
---------------------------------------------------------------------------------------------------------------------------- Searching Unsorted List of Numbers - Algorithm in GRAFIT Open Learning Implemented - 21 January 2018 ---------------------------------------------------------------------------------------------------------------------------- An algorithm to search list of numbers better than bruteforce search mentioned in GRAFIT Course Notes: https://github.com/shrinivaasanka/Grafit/blob/master/course_material/ComputerScienceMiscellaneous/ComputerScienceMiscellaneous_CourseNotes.txt has been implemented in NeuronRain AsFer.
Srinivasan Kannan
More... almost 9 years ago
Updated Requirements.txt
Srinivasan Kannan
More... almost 9 years ago
Updated AsFer Design Document - openSSL for cloud_move
Srinivasan Kannan
More... almost 9 years ago
------------------------------------------------------------------------------------------------------------------------- Secure Neuro Currency Cloud Perfect Forward Move - OpenSSL client and server - 19 January 2018 ------------------------------------------------------------------------------------------------------------------------- - Makefile updated include -DOPENSSL #ifdef option for compiling SSL client-server headers - license headers updated - new header asfercloudmove_openssl.h has been added to repository for invoking openSSL client and server functions in opensslclient.h and opensslserver.h - An example fictitious X.509 cert.pem and key.pem have been created by openssl utility for certificate verification - new headers opensslclient.h and opensslserver.h have been added to repository which define openSSL client and server functions (these are reference examples in www.openssl.org Wiki changed for cloud move) - logs for SSL cloud_move client and server have been committed to cloud_move/testlogs/
Srinivasan Kannan
More... almost 9 years ago
Updated Requirements.txt
Srinivasan Kannan
More... almost 9 years ago
Updated AsFer Design Document - N/logN Desmos plot
Srinivasan Kannan
More... almost 9 years ago
Desmos Calculator Function plotter graph for N/logN (number of PRAM processors in Computational Geometric Factorization). For factoring large integers number of parallel processors required is not too huge.
Srinivasan Kannan
More... almost 9 years ago
Updated AsFer Design Document - Parallel Integer Sorting
Srinivasan Kannan
More... almost 9 years ago
---------------------------------------------------------------------------------------------------------------------------------------------- 498. (THEORY and FEATURE-DONE) Computational Geometric Factorization - Non-Persistent Tile Segments - 31 bit Single Node Dual Core Spark Benchmarks - Primality Testing - Multiplicative Partition - Factorisatio Numerorum - 12 January 2018 - related to 495 ---------------------------------------------------------------------------------------------------------------------------------------------- 31-bit integer 2147483647 has been factorized by searching non-persisted pixelated hyperbolic tile segments. Only trivial factors are found and is a mersenne prime. This integer is the maximum permissible limit for xrange() and printed by python sys module (sys.maxsize). Similar to integer partition function which is number of ways of splitting an integer as sum of smaller integers, multiplicative partition function f(n) = number of ways of unordered factorizations of n. This function has an upperbound of : a{n} <= n(exp{{log n * logloglog n}/{loglog n}})^{{-1+o(1)}}.
Srinivasan Kannan
More... almost 9 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... almost 9 years ago
--------------------------------------------------------------------------------------------------------------------------------- Computational Geometric Factorization - Non-Persistent Tile Segments - 30 bit Single Node Dual Core Spark Benchmarks - 11 January 2018 --------------------------------------------------------------------------------------------------------------------------------- (#) Benchmarks for factoring another 30-bit integer 999994437 on single node dual core Spark cluster have been committed to python-src/testlogs/ (#) Similar to previous 30 bit integer, time utility prints 40 minutes 56 seconds while the actual Spark duration is between first and last factors: 18/01/11 13:34:34 INFO Utils: /home/shrinivaasanka/Krishna_iResearch_OpenSource/GitHub/asfer-github-code/python-src/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py has been previously copied to /tmp/spark-5630f619-b8a7-46b8-b15b-7296508792c5/userFiles-44c23be8-a7b3-48d3-bfd7-98acf1971e10/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py ================================================= Factor is = 1 ================================================= ================================================= Factor is = 3 ================================================= ================================================= Factor is = 9 ================================================= ================================================= Factor is = 13 ================================================= ================================================= Factor is = 23 ================================================= ================================================= Factor is = 27 ================================================= ================================================= Factor is = 39 ================================================= ================================================= Factor is = 69 ================================================= ================================================= Factor is = 97 ================================================= ================================================= Factor is = 117 ================================================= ================================================= Factor is = 207 ================================================= ================================================= Factor is = 291 ================================================= ================================================= Factor is = 299 ================================================= ================================================= Factor is = 351 ================================================= ================================================= Factor is = 621 ================================================= ================================================= Factor is = 873 ================================================= ================================================= Factor is = 897 ================================================= ================================================= Factor is = 1261 ================================================= ================================================= Factor is = 1277 ================================================= ================================================= Factor is = 2231 ================================================= ================================================= Factor is = 2619 ================================================= ================================================= Factor is = 2691 ================================================= ================================================= Factor is = 3783 ================================================= ================================================= Factor is = 3831 ================================================= ================================================= Factor is = 6693 ================================================= ================================================= Factor is = 8073 ================================================= ================================================= Factor is = 11349 ================================================= ================================================= Factor is = 11493 ================================================= ================================================= Factor is = 16601 ================================================= ================================================= Factor is = 20079 ================================================= ================================================= Factor is = 29003 ================================================= ================================================= Factor is = 29371 ================================================= ================================================= Factor is = 34047 ================================================= ================================================= Factor is = 34479 ================================================= ================================================= Factor is = 49803 ================================================= ================================================= Factor is = 60237 ================================================= ================================================= Factor is = 87009 ================================================= ================================================= Factor is = 88113 ================================================= ================================================= Factor is = 123869 ================================================= ================================================= Factor is = 149409 ================================================= ================================================= Factor is = 261027 ================================================= ================================================= Factor is = 264339 ================================================= ================================================= Factor is = 371607 ================================================= ================================================= Factor is = 381823 ================================================= ================================================= Factor is = 448227 ================================================= ================================================= Factor is = 783081 ================================================= ================================================= Factor is = 793017 ================================================= ================================================= Factor is = 1114821 ================================================= ================================================= Factor is = 1145469 ================================================= ================================================= Factor is = 1610297 ================================================= ================================================= Factor is = 2848987 ================================================= ================================================= Factor is = 3344463 ================================================= ================================================= Factor is = 3436407 ================================================= ================================================= Factor is = 4830891 ================================================= ================================================= Factor is = 8546961 ================================================= ================================================= Factor is = 10309221 ================================================= ================================================= Factor is = 14492673 ================================================= ================================================= Factor is = 25640883 ================================================= ================================================= Factor is = 37036831 ================================================= ================================================= Factor is = 43478019 ================================================= ================================================= Factor is = 76922649 ================================================= ================================================= Factor is = 111110493 ================================================= 18/01/11 13:48:32 INFO PythonRunner: Times: total = 837634, boot = 641, init = 37, finish = 836956 18/01/11 13:48:32 INFO Executor: Finished task 0.0 in stage 0.0 (TID 0). 1529 bytes result sent to driver 18/01/11 13:48:32 INFO TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1, localhost, executor driver, partition 1, PROCESS_LOCAL, 6208 bytes) 18/01/11 13:48:32 INFO Executor: Running task 1.0 in stage 0.0 (TID 1) ================================================= Factor is = 333331479 ================================================= 18/01/11 13:48:32 INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 838426 ms on localhost (executor driver) (1/3)
Srinivasan Kannan
More... almost 9 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 496. (THEORY and FEATURE-DONE) Computational Geometric Factorization Update - Local Tile Computation and necessity for storage removed - 10 January 2018 - 30-bit integer dual core single node Spark Cluster benchmark - related to 495 -------------------------------------------------------------------------------------------------------------------------------------------- As mentioned in previous section, reading tiles from storage in SparkContext.parallelize() is a serious bottleneck. In this commit, tile interval storage is obviated and each foreach() invokes a function which computes non-persisted tile interval locally and does binary search to print factors. SparkContext.parallelize() takes xrange() as argument. Python xrange() is an optimized implementation of list and returns XRange object.XRange object does not store all the elements in memory and is more like an iterator. A thirty bit integer has been factored and numbers are below: --------------------------- Factorization of 921234437: --------------------------- 18/01/10 17:56:56 INFO Utils: /home/shrinivaasanka/Krishna_iResearch_OpenSource/GitHub/asfer-github-code/python-src/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py has been previously copied to /tmp/spark-9fba8482-8ca1-4022-9bcc-1c285a8e1f58/userFiles-3e4bf574-9b1a-44c5-8b96-3873e96dc6c3/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py Factor is = 1 Factor is = 17 Factor is = 19 Factor is = 59 Factor is = 323 Factor is = 1003 Factor is = 1121 Factor is = 19057 Factor is = 48341 Factor is = 821797 Factor is = 918479 Factor is = 2852119 Factor is = 15614143 Factor is = 48486023 Factor is = 54190261 18/01/10 18:08:51 INFO PythonRunner: Times: total = 714516, boot = 649, init = 23, finish = 713844
Srinivasan Kannan
More... almost 9 years ago
Updated AsFer Design Document
Srinivasan Kannan
as K.Srinivasan
More... almost 9 years ago
Updated AsFer Design Document
Srinivasan Kannan
as K.Srinivasan
More... almost 9 years ago
Updated AsFer Design Document
Srinivasan Kannan
as K.Srinivasan
More... almost 9 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... almost 9 years ago
--------------------------------------------------------------------------------------------------------------------- Complement Diophantine Map - Converts a Partial Function to Total Function --------------------------------------------------------------------------------------------------------------------- (#) Sum of Four Squares diophantine solutions are quadruples (a,b,c,d) which form a subset of all possible tuples and thus complement function unknows-to-parameter map is partial. (#) This is remedied by extending the parameter codomain by adding -1 as additional possible parameter value and mapping all unmapped tuples in domain to -1. Resultant map is Total defined for all possible unknown tuples. (#) logs for this have been committed to testlogs/
Srinivasan Kannan
More... almost 9 years ago
Updated AsFer Design Document
Srinivasan Kannan
as K.Srinivasan
More... almost 9 years ago
Updated AsFer Design Document
Srinivasan Kannan
as K.Srinivasan
More... almost 9 years ago